resnet+xgb.py

from keras.applications.resnet50 import ResNet50
from scipy.misc import imresize

resnet=ResNet50()
imgs_new=[]

for i,img in enumerate(imgs_new_rand):
    imgs_new[i]=imresize(img,(224,224,3))
    
resnet_preds=resnet.predict(imgs_new)

from sklearn.model_selection import train_test_split
train_idx,val_idx=train_test_split(range(len(resnet_preds)),test_size=0.2)

trn,val=resnet_preds[train_idx],resnet_preds[val_idx]
y,y_val=tags_encoded[train_idx],tags_encoded[val_idx]

import xgboost as xgb

clf = xgb.XGBClassifier(max_depth=10, n_estimators=1000,min_child_weight=9,learning_rate=0.01,
                       nthread=8, subsample=0.80,colsample_bytree=0.80,seed=4242)

clf.fit(trn , y,eval_set=[(val, y_val)], eval_metric='mlogloss', verbose=True, early_stopping_rounds=50)